Research Scientist, Google Brain
- Date & time
- Monday, February 14, 2022 · 1:00 PM
- Location
- Online (live stream)
Abstract
Energy-based models (EBMs) are an appealing class of probabilistic models that can be learned from unlabeled data, but two challenges remain for training them on high-dimensional datasets: maximum-likelihood learning requires expensive MCMC sampling, and energy potentials learned with non-convergent MCMC can be highly biased. I present two algorithms to tackle these challenges: (1) Diffusion Recovery Likelihood, which tractably learns and samples from a sequence of EBMs trained on increasingly noisy versions of a dataset, and (2) Flow Contrastive Estimation, which jointly estimates an EBM and a flow-based model via a shared adversarial value function.
About the speaker
Ruiqi Gao is a research scientist at Google Brain. Her research interests are in statistical modeling and learning, with a focus on generative models and representation learning. She received her Ph.D. in statistics from UCLA in 2021 advised by Song-Chun Zhu and Ying Nian Wu.